Group Testing
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (28 February 2014) | Viewed by 240
Special Issue Editor
Interests: coding and information theory; pseudorandomness and extractor theory; explicit constructions; complexity theory; sparse recovery; approximation algorithms
Special Issue Information
Dear Colleagues,
This special issue of Algorithms is devoted to combinatorial group testing. As a sub-area of sparse recovery, group testing aims for efficient identification of a number of individuals (known as "positives") within a very large population. This is achieved by a small number of carefully designed "group tests", where each test reveals whether a predetermined set of individuals contains any positives. This simple abstract model can capture surprisingly many problems of theoretical and practical interest, where the population of individuals may correspond to biological samples, data points in a sensor network or a data stream, links or nodes in a large computer network or a social network, etc. In these problems, the naive strategy for collection of information may be prohibitive due to the costs related to preparation of the tests, storage issues of large amounts of data, communication costs, or other resource constraints. This motivates further development of efficient test designs, fast recovery algorithms, novel applications of group testing in practical and theoretical problems, as well as the study of variations of group testing in different models. We hereby invite articles presenting original research on group testing to this special issue and hope to receive many high-quality submissions.
Dr. Mahdi Cheraghchi
Guest Editor
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